Results 21 to 30 of about 13,519,689 (294)
Entropy, 2020 Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
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Structure Learning of H-Colorings [PDF]
ACM Transactions on Algorithms, 2020 We study the following structure learning problem forH-colorings. For a fixed (and known) constraint graphHwithqcolors, given access to uniformly randomH-colorings of an unknown graphG=(V,E), how many samples are required to learn the edges of G? We give a characterization of the constraint graphs Hfor which the problem is identifiable for every Gand ...
Antonio Blanca +3 more
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The variance of causal effect estimators for binary v-structures
Journal of Causal Inference, 2022 Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
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The Structuration of Organizational Learning [PDF]
Human Relations, 2003 Although it is currently common to speak of organizational learning, this notion is still surrounded by conceptual confusion. It is unclear how notions like learning, knowledge and cognitive activities can be applied to organizations. Some authors have tried to unravel the conceptual and ontological problems by giving an account of the role of ...
Berends, J.J. +2 more
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IEEE Access, 2022 Learning the structure of Bayesian networks is a challenging problem because it is a NP-Hard problem. As an excellent search & score based method, the K2 algorithm strongly depends on the input of global order of all nodes to ensure the result is ...
Kunhua Zhong +3 more
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Expanding the notion of global learning: Turkish-Dutch teens’ networked configurations for learning [PDF]
, 2020 Digital technology facilitate interactions between learners and resources at a global level. New learner prototypes are therefore proposed, such as the notion of the global learner.
Ünlüsoy, Asli +5 more
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Atomistic structure learning [PDF]
The Journal of Chemical Physics, 2019 One endeavor of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here, we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D structures and planar compounds atom by atom.
Mathias S. Jørgensen +6 more
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A Novel BN Learning Algorithm Based on Block Learning Strategy
Sensors, 2020 Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and ...
Xinyu Li, Xiaoguang Gao, Chenfeng Wang
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Structure-preserving deep learning [PDF]
European Journal of Applied Mathematics, 2021 Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved in applying deep learning: most deep learning methods require the solution of hard optimisation problems,
CELLEDONI, E. +6 more
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Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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